In the world of number-crunching baseball folks who enjoy poring over reports describing the outcome of every two-seam fastball thrown in the majors over the past 10 years, it’s sacrilege to admit that advanced analytics might at times be, well, boring. That kind of talk could get you ejected from “The Big Bang Theory” fan club.

Sig Mejdal isn’t afraid to say that his study of theoretical approaches to numbers was tedious at times. But when it came to collecting data that would feed his passion for baseball, Mejdal was fully engaged. We’re talking about a guy who while playing the board game All-Star Baseball as a pre-teen used regression analysis (“a statistical technique that enables one to tease out factors that add predictability”) to figure out if players’ skills would deteriorate from season to season.

The Houston Astros are delighted that Mejdal fought through the drearier subject matter in pursuit of degrees in mechanical and aerospace engineering (undergrad) and human factors engineering and operations research (masters) to develop the foundation necessary to be their director of decision sciences.

That’s right, baseball fans, director of decision sciences. The kids you picked last on the sandlots have made it to the majors.

Mejdal’s role with the Astros illustrates just how much teams continue to turn to people like him—and not just tobacco juice-stained bird dogs—to chart their personnel decisions. When Houston hired Jeff Luhnow as its GM before the 2012 season, he brought Mejdal with him from the St. Louis Cardinals and immediately transformed the Astros from baseball Luddites into a leader among the advanced stats crowd.

“When I was a kid, I never would have imagined a baseball team would have been hiring a man with a background like myself,” says Mejdal, who spent six years with the Cardinals. “Then the Moneyball book came out, and I thought teams would climb all over themselves to get their own geek.

“I was wrong, but I eventually found an opportunity in St. Louis.”

Some teams continue to resist loading up their front offices with analysts like Mejdal, but the number committing resources to people like him is growing. Teams understand the need to blend old-fashioned scouting and evaluation methods with more modern statistical scrutiny gleaned from computer programs and mathematical protocols designed to help them limit financial risk.

Titles like director of decision sciences and director of baseball analytics have joined director of baseball operations on team org charts. Clubs are blending the two worlds into one model that allows them to assess players and prospects in the most thorough manner possible. It’s a process that is in its relative infancy, and there are some conflicting ideas on how best to integrate the analysts with the “baseball men.” But it’s happening, and it’s changing front offices.

The growing influence of advanced metrics was particularly evident during the debate over who should have won the 2012 AL MVP award.

Others, including a lot of front office types, not to mention 71 players in SN’s balloting, believed the Los Angeles Angels’ Mike Trout should have won, in part because of his more complete game—demonstrated by his higher WAR (wins above replacement, which measures fielding and baserunning, along with hitting and power stats).

It was a classic showdown, and it demonstrated the two sides of the, ahem, equation.

“You process all the information, and if you’re able to digest it all, you’re more likely to make better decisions,” says San Francisco Giants VP of baseball operations Bobby Evans, who says Cabrera’s Triple Crown was “like a trump card” for voters. “If you isolate some information without looking at everything, you could make a mistake.“All of the things have to be looked at to make a full and complete decision.”

THE TRUE MEASURE OF WAR

When it came to the debate over whether Trout or Cabrera was the AL MVP, a lot of people asked a simple question: WAR, what is it good for?

The Society for American Baseball Research believes it’s great for just about everything. SABR president Vince Gennaro says “the concept of WAR is to be all-inclusive.”

The thinking: No other one stat is able to give such a complete look at a player. Batting average is great, but it doesn’t measure baserunning effectiveness. On-base percentage is fine, but it can’t give a look at a player’s fielding ability. WAR, which tells how many wins a player is worth over the average replacement performer, can provide a thorough assessment in one number. It’s the difference between evaluating players in black-and-white analog and vivid, colorful HD.

“WAR looks at baserunning and fielding, playing time and the player’s total value,” says Dave Cameron, managing editor of fangraphs.com, a popular baseball analysis website. “It’s an encompassing metric that doesn’t just look at hitting.

“Cabrera was the best hitter last year; there’s no doubt. But Trout was the second best hitter and a better fielder and baserunner.”

It’s easy to argue that Cabrera’s stronger finish to the regular season—.344 with 19 homers and 54 RBIs after July vs. .287-12-28 for Trout—was a big reason he won the MVP. So was the fact that he led the Tigers into the postseason, while the Angels failed to qualify.

But people such as Cameron and other SABR members like Mejdal (he joined while still in grade school in San Jose) look at Trout’s 2.9 advantage (10.0-7.1, according to fangraphs.com data) in WAR, a huge disparity in the measurement, as a major argument in his favor.

Because Trout was so much better in terms of baserunning (a league-leading 49 stolen bases to four for Cabrera) and as a fielder (he saved 21 runs with his glove), he clearly is a better all-around player than Cabrera. Many sabermetricians will tell you that Trout’s season was one of the greatest complete performances in baseball history, certainly grounds for an MVP selection.

“If you are picking the player of the year between Trout and Cabrera, it isn’t even close,” Gennaro says. “Trout wins. What Cabrera did was historic. He was absolutely the best hitter in baseball. But if you’re looking at who was most valuable, Trout was incredible on so many different levels.”

WAR is an increasingly valued indicator, but it is hardly the only advanced statistical analysis out there. Mainframes are churning out a slew of numbers to help teams assess the relative worth of players.

“With high salaries at stake, you can’t intuitively make eight and nine-figure decisions,” Gennaro says.

So, teams rely on metrics such as FIELDf/x, which measures a player’s defensive value. Sportvision has cameras in every stadium to measure how much ground a fielder covers in how many seconds. It’s not as much a measure of whether a player makes a catch as it is his individual fielding ability. Teams can use that data to evaluate how a player will fit in a certain stadium (wide-open spaces vs. a bandbox) and with a certain pitching staff (fly ball vs. ground ball).

“This will get away from the outcome and look at raw skills,” Cameron says. “It’s measuring reaction time. How far did he go, how long did it take him and what angle did he take?”

Last year, when the Tampa Bay Rays played the New York Yankees, sharply hit balls up the middle by New York hitters were gobbled up because Tampa Bay manager Joe Maddon had studied his fielders’ tendencies and matched them to the Yankees’ hitting patterns.

The goal of advanced metrics is to predict what will happen, based on what has happened. Armed with that information and a protocol that is team-specific, clubs can decide how long a free-agent contract offer should be and for how much money. Digging deeper into the information can help with that decision.

THE EVOLUTION OF STATISTICS

When teams decide to put a greater emphasis on innovative statistical analysis, they don’t change the basic numbers that have been part of the game for more than 100 years. It’s not as if batting average is supplanted by how much a player bends the brim of his cap.

“We may be putting (statistics) together in novel and creative ways,” says Keith Woolner, the Cleveland Indians' director of baseball analytics. “Technology has allowed us to collect and process more information.”

The Indians are enthusiastic users of advanced metrics. Woolner has been with them for more than five years but spent his professional career before that in the software industry. Some scoff at his presence in a front office, but given the stakes involved in contracts today, it makes sense to have someone capable of viewing baseball through a lens similar to those used by other industries.

“Everybody is trying to run their business better, make better decisions, outmaneuver the competition and make a better product,” Woolner says. “Baseball is like that. It’s not surprising that things that have applications in other industries should be applied to sports.”

The trick is to blend heavily mined data with the experience and intuition found in the game’s human element. That seems to be the variable that differentiates the 30 MLB teams. No two give the same weight to a particular analytic, and no two balance the numbers and scouting sides quite the same. And it’s important that no matter what formula a team applies that there is continual evaluation and evolution of the process.

“We are constantly improving our processes by examining the feedback of how players do in real life,” Mejdal says. “If a player zigged when we thought he would zag, that goes into the system right away.”

This is not a fad. Teams are dedicating significant resources to it, and they are moving from using interns to hiring people full-time to assess the numbers. Players aren’t moving to the advanced metrics side in large numbers, but the discussion has reached them, and a few, like Tampa Bay’s Sam Fuld, are embracing the concepts. Fuld even appeared on a Rays sabermetrics-themed TV broadcast last year.

The Kansas City Royals, who previously were among teams not so interested in evaluating large data sets (the Philadelphia Phillies are another), just elevated Mike Groopman to director of baseball analytics and have begun using FIELDf/x to measure fielders’ comparative strengths and weaknesses. And it’s likely the Royals took a look at James Shields’ strong 2012 WAR (4.3) before trading for him in December. They also focused on how he remained effective for a sixth consecutive year despite having to become more of a fastball pitcher than one who often relied on his curveball. That indicates the ability to adapt to remain effective.

“We are giving expanded effort to integrate advanced statistical analysis into all aspects of our baseball operations,” says Groopman, who holds degrees from Columbia in Middle Eastern languages and cultures and in linguistics. “When we approach problems and questions in the baseball world, we try to leverage increased data to answer them.”